Vol. 7, Issue 2, Part E (2025)

Artificial Intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. Large amounts of biological data stored in global databases are the building blocks for machine learning and deep learning methods. They make it easier to find patterns and models that can help find therapeutically active molecules with less time, work, and money. Machine learning and deep learning technology are vital in drug design and development. We have applied these algorithms to various drug discovery processes such as protein structure prediction, toxicity prediction, oral bioavailability prediction, de novo design of new chemical scaffolds, structure-based and ligand-based virtual screening, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, and clinical trial design. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into pharmaceutical sciences has catalyzed transformative advancements across drug discovery, clinical development, manufacturing, and post-market surveillance.